Ensemble methods

Fraud Detection in Python

Charlotte Werger

Data Scientist

What are ensemble methods: bagging versus stacking

Fraud Detection in Python

Stacking ensemble methods

Fraud Detection in Python

Why use ensemble methods for fraud detection

Ensemble methods:

  • Are robust
  • Can help you avoid overfitting
  • Can typically improve prediction performance
  • Are a winning formula at prestigious Kaggle competitions
Fraud Detection in Python

Voting classifier

from sklearn.ensemble import VotingClassifier

clf1 = LogisticRegression(random_state=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()

ensemble_model = VotingClassifier(estimators=[('lr', clf1),
('rf', clf2), ('gnb', clf3)], voting='hard')

ensemble_model.fit(X_train, y_train)
ensemble_model.predict(X_test)

VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), 
('gnb', clf3)], voting='soft', weights=[2,1,1])
Fraud Detection in Python

Reliable labels for fraud detection

Fraud Detection in Python

Let's practice!

Fraud Detection in Python

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